Dataset Information

Development and Validation of Predictive Assessment of Complicated Diverticulitis Score.

ABSTRACT: The prevalence of acute diverticulitis (AD) has progressively increased in recent decades, with correspondingly greater morbidity and mortality. The aim of the study is to develop a predictive score to identify patients with the highest risk of complicated AD. The clinical records of 1089 patients referred to the emergency department (ED) over a five-year period were reviewed. In multivariate analysis, male sex (p < 0.001), constipation (p = 0.002), hemoglobin < 11.9 g/dL (p < 0.001), C reactive protein > 80 mg/L (p < 0.001), severe obesity (p = 0.049), and no proton pump inhibitor treatment (p = 0.003) were independently associated with complicated AD. The predictive assessment of complicated (PACO)-diverticulitis (D) score, including these six variables, was applied to the retrospective cohort and then validated prospectively in a cohort including 282 patients. It categorized patients into three risk classes for complicated AD. The PACO-D score showed fair discrimination for complicated AD with an area under the receiver operating characteristic curve of 0.674 and 0.648, in the retrospective and prospective cohorts, respectively. The PACO-D score could be a practical clinical tool to identify patients at highest risk for complicated AD referred to the ED so that appropriate diagnostic and therapeutic resources could be appropriately allocated. Further external validation is needed to confirm these results.

SUBMITTER: Covino M

PROVIDER: S-EPMC7911244 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Development and Validation of Predictive Assessment of Complicated Diverticulitis Score.

Covino Marcello M Papa Valerio V Tursi Antonio A Simeoni Benedetta B Lopetuso Loris Riccardo LR Vetrone Lorenzo Maria LM Franceschi Francesco F Rapaccini Gianludovico G Gasbarrini Antonio A Gasbarrini Antonio A Papa Alfredo A

Journal of personalized medicine 20210129 2

The prevalence of acute diverticulitis (AD) has progressively increased in recent decades, with correspondingly greater morbidity and mortality. The aim of the study is to develop a predictive score to identify patients with the highest risk of complicated AD. The clinical records of 1089 patients referred to the emergency department (ED) over a five-year period were reviewed. In multivariate analysis, male sex (p < 0.001), constipation (p = 0.002), hemoglobin < 11.9 g/dL (p ...[more]

PMID: 33572940

Similar Datasets

Project description:BackgroundPatients suffering from major trauma often experience complications such as sepsis. The early recognition of patients at high risk of sepsis after trauma is critical for precision therapy. We aimed to derive and validate a novel predictive score for sepsis risk using electronic medical record (EMR) data following trauma.Materials and methodsClinical and laboratory variables of 684 trauma patients within 24 h after admission were collected, including 411 patients in the training cohort and 273 in the validation cohort. The least absolute shrinkage and selection operator (LASSO) technique was adopted to identify variables contributing to the early prediction of traumatic sepsis. Then, we constructed a traumatic sepsis score (TSS) using a logistic regression model based on the variables selected in the LASSO analysis. Moreover, we evaluated the discrimination and calibration of the TSS using the area under the curve (AUC) and the Hosmer-Lemeshow (H-L) goodness-of-fit test.ResultsBased on the LASSO, seven variables (injury severity score, Glasgow Coma Scale, temperature, heart rate, albumin, international normalized ratio, and C-reaction protein) were selected for construction of the TSS. Our results indicated that the incidence of sepsis after trauma increased with an increasing TSS (P trend = 7.44 × 10-21 for the training cohort and P trend = 1.16 × 10-13 for the validation cohort). The areas under the receiver operating characteristic (ROC) curve of TSS were 0.799 (0.757-0.837) and 0.790 (0.736-0.836) for the training and validation datasets, respectively. The discriminatory power of our model was superior to that of a single variable and the sequential organ failure assessment (SOFA) score (P < 0.001). Moreover, the TSS was well calibrated (P > 0.05).ConclusionsWe developed and validated a novel TSS with good discriminatory power and calibration for the prediction of sepsis risk in trauma patients based on the EMR data.

Project description:BackgroundNon-attendance to scheduled hospital outpatient appointments may compromise healthcare resource planning, which ultimately reduces the quality of healthcare provision by delaying assessments and increasing waiting lists. We developed a model for predicting non-attendance and assessed the effectiveness of an intervention for reducing non-attendance based on the model.MethodsThe study was conducted in three stages: (1) model development, (2) prospective validation of the model with new data, and (3) a clinical assessment with a pilot study that included the model as a stratification tool to select the patients in the intervention. Candidate models were built using retrospective data from appointments scheduled between January 1, 2015, and November 30, 2018, in the dermatology and pneumology outpatient services of the Hospital Municipal de Badalona (Spain). The predictive capacity of the selected model was then validated prospectively with appointments scheduled between January 7 and February 8, 2019. The effectiveness of selective phone call reminders to patients at high risk of non-attendance according to the model was assessed on all consecutive patients with at least one appointment scheduled between February 25 and April 19, 2019. We finally conducted a pilot study in which all patients identified by the model as high risk of non-attendance were randomly assigned to either a control (no intervention) or intervention group, the last receiving phone call reminders one week before the appointment.ResultsDecision trees were selected for model development. Models were trained and selected using 33,329 appointments in the dermatology service and 21,050 in the pneumology service. Specificity, sensitivity, and accuracy for the prediction of non-attendance were 79.90%, 67.09%, and 73.49% for dermatology, and 71.38%, 57.84%, and 64.61% for pneumology outpatient services. The prospective validation showed a specificity of 78.34% (95%CI 71.07, 84.51) and balanced accuracy of 70.45% for dermatology; and 69.83% (95%CI 60.61, 78.00) for pneumology, respectively. The effectiveness of the intervention was assessed on 1,311 individuals identified as high risk of non-attendance according to the selected model. Overall, the intervention resulted in a significant reduction in the non-attendance rate to both the dermatology and pneumology services, with a decrease of 50.61% (p<0.001) and 39.33% (p=0.048), respectively.ConclusionsThe risk of non-attendance can be adequately estimated using patient information stored in medical records. The patient stratification according to the non-attendance risk allows prioritizing interventions, such as phone call reminders, to effectively reduce non-attendance rates.

Project description:Despite the best efforts of intensive care units (ICUs) professionals, the extubation failure rates in mechanically ventilated patients remain in the range of 5%-30%. Extubation failure is associated with increased risk of death and longer ICU stay. This study aimed to identify respiratory and non-respiratory parameters predictive of extubation outcome, and to use these predictors to develop and validate an "Extubation Predictive Score (ExPreS)" that could be used to predict likelihood of extubation success in patients receiving invasive mechanical ventilation (IMV). Derivation cohort was composed by patients aged ≥18 years admitted to the ICU and receiving IMV through an endotracheal tube for >24 hours. The weaning process followed the established ICU protocol. Clinical signs and ventilator parameters of patients were recorded during IMV, in the end phase of weaning in pressure support ventilation (PSV) mode, with inspiratory pressure of 7 cm H2O over the PEEP (positive end expiratory pressure). Patients who tolerated this ventilation were submitted to spontaneous breathing trial (SBT) with T-tube for 30 minutes. Those who passed the SBT and a subsequent cuff-leak test were extubated. The primary outcome of this study was extubation success at 48 hours. Parameters that showed statistically significant association with extubation outcome were further investigated using the receiver operating characteristics (ROC) analysis to assess their predictive value. The area under the curve (AUC) values were used to select parameters for inclusion in the ExPreS. Univariable logistic regression analysis and ROC analysis were performed to evaluate the performance of ExPreS. Patients' inclusion and statistical analyses for the prospective validation cohort followed the same criteria used for the derivation cohort and the decision to extubate was based on the ExPreS result. In the derivation cohort, a total of 110 patients were extubated: extubation succeeded in 101 (91.8%) patients and failed in 9 (8.2%) patients. Rapid shallow-breathing index (RSBI) in SBT, dynamic lung compliance, duration of IMV, muscle strength, estimated GCS, hematocrit, and serum creatinine were significantly associated with extubation outcome. These parameters, along with another parameter-presence of neurologic comorbidity-were used to create the ExPreS. The AUC value for the ExPreS was 0.875, which was higher than the AUCs of the individual parameters. The total ExPreS can range from 0 to 100. ExPreS ≥59 points indicated high probability of success (OR = 23.07), while ExPreS ≤44 points indicated low probability of success (OR = 0.82). In the prospective validation cohort, 83 patients were extubated: extubation succeeded in 81 (97.6%) patients and failed in 2 (2.4%) patients. The AUC value for the ExPreS in this cohort was 0.971. The multiparameter score that we propose, ExPreS, shows good accuracy to predict extubation outcome in patients receiving IMV in the ICU. In the prospective validation, the use of ExPreS decreased the extubation failure rate from 8.2% to 2.4%, even in a cohort of more severe patients.

Dataset Information

Development and Validation of Predictive Assessment of Complicated Diverticulitis Score.

Publications

Development and Validation of Predictive Assessment of Complicated Diverticulitis Score.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets